from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(against_lib="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 prediction (58.33%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	e70d8e7886766d41f30889506baa1e67	2cf49829391aa7891e877f2ed070adf0	1.944462	0.149903	NaN	0.000411	0.001944	brute	-1	1	0.663	0.175844	0.005268	0.687	11.057867	11.062828	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	22d5eac4a4146149b35eae6914921514	2cf49829391aa7891e877f2ed070adf0	2.760593	0.035551	NaN	0.000290	0.002761	brute	-1	5	0.757	0.174119	0.000250	0.742	15.854663	15.854679	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	2.031129	0.008811	NaN	0.000394	0.002031	brute	1	100	0.882	0.209325	0.000582	0.875	9.703241	9.703279	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	0.020564	0.000214	NaN	0.000039	0.020564	brute	1	100	1.000	0.008704	0.000290	0.000	2.362613	2.363920	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	2.763092	0.032432	NaN	0.000290	0.002763	brute	-1	100	0.882	0.209494	0.002708	0.875	13.189357	13.190459	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	0.024565	0.002537	NaN	0.000033	0.024565	brute	-1	100	1.000	0.008928	0.000887	0.000	2.751440	2.764992	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	efac583623711d395ddae7a1e881ff68	2cf49829391aa7891e877f2ed070adf0	2.010532	0.001644	NaN	0.000398	0.002011	brute	1	5	0.757	0.174371	0.000837	0.742	11.530172	11.530304	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	b79d63bb7670c492de5c3befac58fe29	2cf49829391aa7891e877f2ed070adf0	1.161442	0.009139	NaN	0.000689	0.001161	brute	1	1	0.663	0.174014	0.002288	0.687	6.674435	6.675012	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	e70d8e7886766d41f30889506baa1e67	88843f54689e3271092f70126e1de585	1.732155	0.015052	NaN	0.000009	0.001732	brute	-1	1	0.896	0.025767	0.000198	0.967	67.222523	67.224508	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	22d5eac4a4146149b35eae6914921514	88843f54689e3271092f70126e1de585	2.637346	0.019484	NaN	0.000006	0.002637	brute	-1	5	0.922	0.026808	0.000165	0.974	98.378771	98.380631	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	16f94fc93942ff7ece860c9b0f64645f	88843f54689e3271092f70126e1de585	1.938979	0.001989	NaN	0.000008	0.001939	brute	1	100	0.929	0.060327	0.002021	0.975	32.140915	32.158949	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2d5f2b2c02d77766434d9e5033b4a76c	88843f54689e3271092f70126e1de585	2.668220	0.018521	NaN	0.000006	0.002668	brute	-1	100	0.929	0.062120	0.005806	0.975	42.952673	43.139885	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	efac583623711d395ddae7a1e881ff68	88843f54689e3271092f70126e1de585	1.928455	0.003849	NaN	0.000008	0.001928	brute	1	5	0.922	0.026792	0.000103	0.974	71.980002	71.980537	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	b79d63bb7670c492de5c3befac58fe29	88843f54689e3271092f70126e1de585	1.054937	0.001912	NaN	0.000015	0.001055	brute	1	1	0.896	0.025884	0.000248	0.967	40.756750	40.758627	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.052	0.0	-1	1	0.048	0.005	0.238	0.239	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.057	0.0	-1	5	0.045	0.000	0.250	0.250	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.007	0.0	1	100	0.046	0.000	0.247	0.247	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.014	0.0	-1	100	0.046	0.000	0.249	0.249	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.004	6.127	0.0	1	5	0.046	0.000	0.283	0.283	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.028	0.0	1	1	0.046	0.000	0.248	0.248	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.377	0.0	-1	1	0.008	0.000	0.514	0.514	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.379	0.0	-1	5	0.008	0.000	0.513	0.513	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.381	0.0	1	100	0.008	0.000	0.507	0.507	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.381	0.0	-1	100	0.008	0.000	0.510	0.510	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.375	0.0	1	5	0.008	0.000	0.517	0.517	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.380	0.0	1	1	0.008	0.000	0.510	0.510	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.944	0.150	0.000	0.002	-1	1	0.176	0.005	11.058	11.063	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.003	0.000	0.024	-1	1	0.009	0.000	2.818	2.818	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.761	0.036	0.000	0.003	-1	5	0.174	0.000	15.855	15.855	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.002	0.000	0.023	-1	5	0.009	0.000	2.720	2.721	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.031	0.009	0.000	0.002	1	100	0.209	0.001	9.703	9.703	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.000	0.000	0.021	1	100	0.009	0.000	2.363	2.364	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.763	0.032	0.000	0.003	-1	100	0.209	0.003	13.189	13.190	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.003	0.000	0.025	-1	100	0.009	0.001	2.751	2.765	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.011	0.002	0.000	0.002	1	5	0.174	0.001	11.530	11.530	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.021	0.000	0.000	0.021	1	5	0.009	0.000	2.407	2.408	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.161	0.009	0.001	0.001	1	1	0.174	0.002	6.674	6.675	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.020	0.000	0.000	0.020	1	1	0.009	0.000	2.322	2.322	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.732	0.015	0.000	0.002	-1	1	0.026	0.000	67.223	67.225	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.002	0.000	0.004	-1	1	0.001	0.000	6.722	6.739	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.637	0.019	0.000	0.003	-1	5	0.027	0.000	98.379	98.381	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.009	0.002	0.000	0.009	-1	5	0.001	0.000	14.430	14.465	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.939	0.002	0.000	0.002	1	100	0.060	0.002	32.141	32.159	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.939	3.953	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.668	0.019	0.000	0.003	-1	100	0.062	0.006	42.953	43.140	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.002	0.000	0.005	-1	100	0.001	0.000	7.286	7.332	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.928	0.004	0.000	0.002	1	5	0.027	0.000	71.980	71.981	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.310	4.324	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.055	0.002	0.000	0.001	1	1	0.026	0.000	40.757	40.759	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.760	2.767	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	fe8267e25dadb302ed498e50fabf7ef4	1eb6cf1a720a225efb91df0b529b0510	0.876317	1.276624	NaN	0.000091	0.000876	kd_tree	-1	1	0.929	0.119179	0.003178	0.910	7.352972	7.355586	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	dc74b969bf622bc24ba3bc62c980983b	1eb6cf1a720a225efb91df0b529b0510	1.097765	0.502636	NaN	0.000073	0.001098	kd_tree	-1	5	0.946	0.206966	0.009610	0.941	5.304086	5.309801	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	41accfcdcd5c50784bedf6164b63de99	1eb6cf1a720a225efb91df0b529b0510	5.663641	0.742520	NaN	0.000014	0.005664	kd_tree	1	100	0.951	0.629654	0.005520	0.940	8.994844	8.995189	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	022f7445d43bb1dbc24dc3106c03cb93	1eb6cf1a720a225efb91df0b529b0510	3.071856	0.116202	NaN	0.000026	0.003072	kd_tree	-1	100	0.951	0.607333	0.004828	0.940	5.057945	5.058105	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	84622ba45e941db642965553529e1941	1eb6cf1a720a225efb91df0b529b0510	1.694613	0.341814	NaN	0.000047	0.001695	kd_tree	1	5	0.946	0.212676	0.001831	0.941	7.968042	7.968337	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	a9455565db1d8e052a783317c99744ff	1eb6cf1a720a225efb91df0b529b0510	0.947502	0.346885	NaN	0.000084	0.000948	kd_tree	1	1	0.929	0.111416	0.000950	0.910	8.504203	8.504513	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	fe8267e25dadb302ed498e50fabf7ef4	edf7cb014c2c0910e1c3aaaf8ae633f2	0.027441	0.020019	NaN	0.000583	0.000027	kd_tree	-1	1	0.891	0.000384	0.000043	0.879	71.388119	71.841598	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	dc74b969bf622bc24ba3bc62c980983b	edf7cb014c2c0910e1c3aaaf8ae633f2	0.022126	0.000991	NaN	0.000723	0.000022	kd_tree	-1	5	0.911	0.000644	0.000021	0.905	34.373395	34.391083	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	41accfcdcd5c50784bedf6164b63de99	edf7cb014c2c0910e1c3aaaf8ae633f2	0.036908	0.014079	NaN	0.000434	0.000037	kd_tree	1	100	0.894	0.004448	0.000025	0.917	8.297963	8.298090	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	022f7445d43bb1dbc24dc3106c03cb93	edf7cb014c2c0910e1c3aaaf8ae633f2	0.040035	0.009418	NaN	0.000400	0.000040	kd_tree	-1	100	0.894	0.005172	0.001425	0.917	7.740200	8.028531	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	84622ba45e941db642965553529e1941	edf7cb014c2c0910e1c3aaaf8ae633f2	0.019368	0.000162	NaN	0.000826	0.000019	kd_tree	1	5	0.911	0.000628	0.000025	0.905	30.859800	30.883629	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	a9455565db1d8e052a783317c99744ff	edf7cb014c2c0910e1c3aaaf8ae633f2	0.018363	0.000173	NaN	0.000871	0.000018	kd_tree	1	1	0.891	0.000381	0.000033	0.879	48.137254	48.315735	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.028	0.029	0.026	0.0	-1	1	0.808	0.084	3.746	3.767	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.635	0.078	0.022	0.0	-1	5	0.731	0.011	4.975	4.975	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.588	0.084	0.022	0.0	1	100	0.776	0.004	4.621	4.621	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.617	0.111	0.022	0.0	-1	100	0.723	0.006	5.003	5.003	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.642	0.060	0.022	0.0	1	5	0.772	0.003	4.719	4.719	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.630	0.062	0.022	0.0	1	1	0.730	0.016	4.973	4.974	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.017	0.0	-1	1	0.005	0.003	0.199	0.230	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.032	0.0	-1	5	0.003	0.003	0.159	0.231	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	100	0.001	0.001	0.414	0.588	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	100	0.001	0.002	0.360	0.597	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.032	0.0	1	5	0.001	0.000	0.646	0.647	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.032	0.0	1	1	0.001	0.000	0.636	0.637	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.876	1.277	0.000	0.001	-1	1	0.119	0.003	7.353	7.356	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	10.127	10.572	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.098	0.503	0.000	0.001	-1	5	0.207	0.010	5.304	5.310	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	7.796	8.268	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.664	0.743	0.000	0.006	1	100	0.630	0.006	8.995	8.995	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	4.014	4.193	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.072	0.116	0.000	0.003	-1	100	0.607	0.005	5.058	5.058	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.769	7.150	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.695	0.342	0.000	0.002	1	5	0.213	0.002	7.968	7.968	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	3.575	3.874	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.948	0.347	0.000	0.001	1	1	0.111	0.001	8.504	8.505	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.816	4.076	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.027	0.020	0.001	0.000	-1	1	0.000	0.000	71.388	71.842	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	24.379	25.386	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.022	0.001	0.001	0.000	-1	5	0.001	0.000	34.373	34.391	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	23.904	24.798	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.037	0.014	0.000	0.000	1	100	0.004	0.000	8.298	8.298	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	6.132	6.459	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.040	0.009	0.000	0.000	-1	100	0.005	0.001	7.740	8.029	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	21.096	21.866	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.019	0.000	0.001	0.000	1	5	0.001	0.000	30.860	30.884	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.467	6.872	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.018	0.000	0.001	0.000	1	1	0.000	0.000	48.137	48.316	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.611	6.971	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.525	0.102	30	0.030	0.0	random	0.378	0.028	1.390	1.394	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.559	0.020	30	0.029	0.0	k-means++	0.406	0.025	1.379	1.382	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.701	0.282	30	0.140	0.0	random	2.648	0.023	2.153	2.153	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.739	0.008	30	0.139	0.0	k-means++	2.808	0.013	2.044	2.044	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	random	0.0	0.0	9.636	15.250	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	0.0	0.0	7.294	14.260	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	k-means++	0.0	0.0	11.743	13.491	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	0.0	0.0	14.270	15.143	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.468	0.000	random	0.0	0.0	7.174	7.735	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	13.085	13.501	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.485	0.000	k-means++	0.0	0.0	6.815	7.381	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	15.454	15.935	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 prediction (50.0%). The observed differences can be found in the diff_adjusted_rand_scores column. The chosen difference threshold is 0.001 for adjusted_rand_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	3db9c030f488d0e8ff350ae449da4627	058b7e3842b587a5c675518f0706f5ee	0.001804	0.000176	20	0.008871	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000418	0.000037	-0.000965	4.313887	4.330867	0.001090
4	KMeans_short	predict	10000	1000	2	ac9b8cec4749455c0d1cd1a92c150716	058b7e3842b587a5c675518f0706f5ee	0.001684	0.000102	20	0.009504	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000433	0.000044	-0.000750	3.886308	3.906478	0.001995
7	KMeans_short	predict	10000	1000	100	3db9c030f488d0e8ff350ae449da4627	7f85b913f395ec54101a6738ea63a9a7	0.002548	0.000289	20	0.313976	0.000003	full	random	20	300	1	1.000000e-16	0.278733	0.000929	0.000072	0.293767	2.741507	2.749833	0.015034
10	KMeans_short	predict	10000	1000	100	ac9b8cec4749455c0d1cd1a92c150716	7f85b913f395ec54101a6738ea63a9a7	0.002426	0.000195	20	0.329744	0.000002	full	k-means++	20	300	1	1.000000e-16	0.317011	0.000921	0.000072	0.256968	2.635393	2.643476	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.072	0.001	20	0.002	0.0	random	0.027	0.005	2.684	2.725	See	See
3	KMeans_short	fit	10000	10000	2	0.208	0.002	20	0.001	0.0	k-means++	0.081	0.001	2.570	2.570	See	See
6	KMeans_short	fit	10000	10000	100	0.189	0.002	20	0.042	0.0	random	0.106	0.001	1.793	1.793	See	See
9	KMeans_short	fit	10000	10000	100	0.550	0.008	20	0.015	0.0	k-means++	0.308	0.004	1.787	1.788	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.000	0.0	4.314	4.331	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	13.461	13.866	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.010	0.000	k-means++	0.000	0.0	3.886	3.906	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	11.537	11.861	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.314	0.000	random	0.001	0.0	2.742	2.750	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	10.410	10.552	See	See
10	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.330	0.000	k-means++	0.001	0.0	2.635	2.643	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	0.000	0.0	9.582	9.769	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	f11be2529b0a77bc8fb5ee8de141e6b2	09c36ed6521e03974d0c134895f56c01	0.000604	0.001094	[20]	1.323917	6.042677e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000599	0.000952	0.55	1.008511	1.893963	0.01
4	LogisticRegression	predict	1000	100	10000	f11be2529b0a77bc8fb5ee8de141e6b2	7daecaea01e5baa8c862f264e3c1c3b0	0.001518	0.000292	[26]	5.270023	1.518020e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.003034	0.000221	0.28	0.500362	0.501686	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.142	0.497	[20]	0.072	0.000	1.974	0.093	5.645	5.651	See	See
3	LogisticRegression	fit	1000	1000	10000	1.106	1.066	[26]	0.072	0.001	0.834	0.020	1.325	1.326	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.001	0.001	[20]	1.324	0.0	0.001	0.001	1.009	1.894	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.000	[20]	0.015	0.0	0.000	0.000	0.379	0.384	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.000	[26]	5.270	0.0	0.003	0.000	0.500	0.502	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.000	[26]	1.096	0.0	0.001	0.000	0.121	0.121	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). The observed differences can be found in the diff_r2_scores column. The chosen difference threshold is 0.001 for r2_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	diff_r2_scores
1	Ridge	predict	1000	1000	10000	a70ae8dc5c059ec88cf27c61016aee2a	14fae294d530397144c65ba3209bf125	0.011523	0.000269	NaN	6.942915	0.000012	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.018706	0.000485	0.122191	0.615986	0.616193	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.173	0.003	0.463	0.0	0.179	0.001	0.968	0.968	See	See
3	Ridge	fit	1000000	1000000	100	1.127	0.097	0.710	0.0	0.304	0.240	3.703	4.715	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.0	6.943	0.0	0.019	0.0	0.616	0.616	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	1.416	0.0	0.000	0.0	0.592	0.636	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	5.669	0.0	0.000	0.0	0.423	0.738	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	0.017	0.0	0.000	0.0	0.601	0.636	See	See